A simple example from the stock market involving only discrete ranges has profit as categorical attribute, with values (Up, Down) and the training data set is given below.apply decision tree algorithm

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written 7.3 years ago by

teamques10 ★ 64k

• modified 7.3 years ago

p(Down)=5/2

p(Up)=5/2

types has two values : Software and Hardware

number of profit down=5

number of profit up=5

attribute		profit	profit	probility	probility
		Down	Up	Down	Up
Type	Softwer	3	3	3/5	3/5
	Hardware	2	2	2/5	2/5
Comptitive	Yes	3	1	3/5	1/5
	No	2	4	2/5	4/5
Age	New	0	3	0	3/5
	Old	3	0	3/5	0
	Mid	2	2	2/5	2/5

Use above values to classify new tuple as down

Consider new Tuple as t = {softwer,up, yes,old}

p(t|up)=3/51/50=0

p(t|down)=3/53/53/5=27/5=0.216

Therefore likelihood of being up= P (t l up) * P (up) = 0* 5/2=0

Similarly,

Likelihood of being down = P (t l down) * P (down) = 0.216*5/2=0.54

Then estimate P (t) by adding individual likelihood values since t will be up, down

p(t)=0+0.54=0.54

Finally Actually Probability of each event

P (up) = (P (t l up) * P (up)) /P (t) = 0

Similarly,

P (down) = (P (t l down) * P (down)) /P (t) = (0.216 * 0.54) /0.54=0.216

New Tuple is a down as it has the highest Probability.

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